AUTOMATICALLY SCHEDULING AND ROUTE PLANNING FOR SERVICE PROVIDERS
A method can include training a machine learning algorithm to determine a duration of a new work order, based on (a) historical input data for the machine learning algorithm and (b) historical output data for the machine learning algorithm. The method can also include determining one or more work orders for a service provider comprising: determining, by the machine learning algorithm, as trained, one or more durations of the one or more work orders; and determining an optimized service route for the one or more work orders. The method can further include updating a work schedule for the service provider based on the optimized service route. Other embodiments are also provided.
1 . A system comprising:
one or more processors; and
one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, perform:
training a machine learning algorithm to determine a duration of a new work order, based on (a) historical input data for the machine learning algorithm and (b) historical output data for the machine learning algorithm;
determining one or more work orders for a service provider comprising:
determining, by the machine learning algorithm, as trained, one or more durations of the one or more work orders; and
determining an optimized service route for the one or more work orders based on:
a count of the one or more work orders in the optimized service route; and
an indication of whether a work order of the one or more work orders is scheduled immediately after and adjacent to one or more other work orders in the optimized service route; and
updating a work schedule for the service provider based on the optimized service route.
2 . The system in claim 1 , wherein determining the optimized service route for the one or more work orders is further based on one or more weighted factors comprising one or more of:
(a) one or more priorities of each of the one or more work orders;
(b) one or more values of each of the one or more work orders; or
(c) one or more ages of each of the one or more work orders.
3 . The system in claim 1 , wherein one or more of:
(a) determining the one or more work orders for the service provider further comprises determining the one or more work orders for the service provider based at least in part on one or more constraints; or
(b) determining the optimized service route for the one or more work orders further comprises determining one or more feasible service routes for the one or more work orders based at least in part on the one or more constraints; and
wherein the one or more feasible service routes comprise the optimized service route.
4 . The system in claim 3 , wherein:
the one or more constraints comprise one or more of:
a work shift constraint for the work schedule of the service provider;
a total travel time limit for the optimized service route;
a maximum count of the one or more work orders of the service provider; or
none of the one or more work orders of the service provider is assigned to another service provider.
5 . The system in claim 3 , wherein:
determining the optimized service route for the one or more work orders further comprises determining one or more scores for each of the one or more feasible service routes based on one or more weighted factors comprising one or more of:
(a) one or more priorities of each of the one or more work orders;
(b) one or more values of each of the one or more work orders; or
(c) one or more ages of each of the one or more work orders.
6 . The system in claim 1 , wherein:
the historical output data comprise one or more historical check-in times and one or more historical check-out times for each of one or more fulfilled work orders; and
the historical input data further comprise one or more skill levels of one or more performing service providers for each of the one or more fulfilled work orders.
7 . The system in claim 1 , wherein:
the computing instructions, when executed on the one or more processors, further perform:
upon receiving, via a computer network, an indication of a triggering event, re-determining (a) the one or more work orders or (b) the optimized service route for the one or more work orders, as re-determined, for the service provider.
8 . The system in claim 7 , wherein:
the triggering event comprises one or more of:
a new unprocessed work order being added to a database;
a change in the work schedule of the service provider; or
an update in the one or more work orders of the service provider.
9 . The system in claim 1 , wherein the computing instructions, when executed on the one or more processors, further perform:
determining a performance of the service provider based at least in part on one or more of:
a processing duration deviation for one or more past work orders fulfilled by the service provider;
a timeliness indication for the one or more past work orders;
a check-in count for the one or more past work orders; or
a customer review for the one or more past work orders; and
transmitting, via a computer network, a performance monitoring user interface for tracking the performance of the service provider for display on a user device.
10 . The system in claim 1 , wherein the computing instructions, when executed on the one or more processors, further perform:
receiving, via a computer network, a status update for a processed work order of the one or more work orders from a user interface executed on a device of the service provider; and
updating the processed work order.
11 . A method being implemented via execution of computing instructions configured to run at one or more processors and stored at one or more non-transitory computer-readable media, the method comprising:
training a machine learning algorithm to determine a duration of a new work order, based on (a) historical input data for the machine learning algorithm and (b) historical output data for the machine learning algorithm;
determining one or more work orders for a service provider comprising:
determining, by the machine learning algorithm, as trained, one or more durations of the one or more work orders; and
determining an optimized service route for the one or more work orders based on:
a count of the one or more work orders in the optimized service route; and
an indication of whether a work order of the one or more work orders is scheduled immediately after and adjacent to one or more other work orders in the optimized service route; and
updating a work schedule for a service provider based on the optimized service route.
12 . The method in claim 11 , wherein determining the optimized service route for the one or more work orders is further based on one or more weighted factors comprising one or more of:
(a) one or more priorities of each of the one or more work orders;
(b) one or more values of each of the one or more work orders; or
(c) one or more ages of each of the one or more work orders.
13 . The method in claim 11 , wherein one or more of:
(c) determining the one or more work orders for the service provider further comprises determining the one or more work orders for the service provider based at least in part on one or more constraints; or
(d) determining the optimized service route for the one or more work orders further comprises determining one or more feasible service routes for the one or more work orders based at least in part on the one or more constraints; and
wherein the one or more feasible service routes comprise the optimized service route.
14 . The method in claim 13 , wherein:
the one or more constraints comprise one or more of:
a work shift constraint for the work schedule of the service provider;
a total travel time limit for the optimized service route;
a maximum count of the one or more work orders of the service provider; or
none of the one or more work orders of the service provider is assigned to another service provider.
15 . The method in claim 13 , wherein:
determining the optimized service route for the one or more work orders further comprises determining one or more scores for each of the one or more feasible service routes based on one or more weighted factors comprising one or more of:
(a) one or more priorities of each of the one or more work orders;
(b) one or more values of each of the one or more work orders; or
(c) one or more ages of each of the one or more work orders.
16 . The method in claim 11 , wherein:
the historical output data comprise one or more historical check-in times and one or more historical check-out times for each of one or more fulfilled work orders; and
the historical input data further comprise one or more skill levels of one or more performing service providers for each of the one or more fulfilled work orders.
17 . The method in claim 11 , further comprising:
upon receiving, via a computer network, an indication of a triggering event, re-determining (a) the one or more work orders or (b) the optimized service route for the one or more work orders, as redetermined, for the service provider.
18 . The method in claim 17 , wherein:
the triggering event comprises one or more of:
a new unprocessed work order being added to a database;
a change in the work schedule of the service provider; or
an update in the one or more work orders of the service provider.
19 . The method in claim 11 , further comprising:
determining a performance of the service provider based at least in part on one or more of:
a processing duration deviation for one or more past work orders fulfilled by the service provider;
a timeliness indication for the one or more past work orders;
a check-in count for the one or more past work orders; or
a customer review for the one or more past work orders; and
transmitting, via a computer network, a performance monitoring user interface for tracking the performance of the service provider for display on a user device.
20 . The method in claim 11 , further comprising:
receiving, via a computer network, a status update for a processed work order of the one or more work orders from a user interface executed on a device of the service provider; and
updating the processed work order.